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With the growth of population and increase of travelling requirements in metropolitan areas, public transit has been recognized as a promising remedy and is playing an ever more important role in sustainable transportation systems. However, the development of the public transit system has not received enough attention until the recent emergence of Bus Rapid Transit (BRT). In the conventional public transit system, little to no communication passes between transit vehicles and the roadside infrastructure, such as traffic signals and loop detectors. But now, thanks to advancements in automatic vehicle location (AVL) systems and wireless communication, real-time and high-resolution information of the movement of transit vehicles has become available, which may potentially facilitate the development of more advanced traffic control and management systems. This dissertation introduces a novel adaptive traffic signal control system, which utilizes the real-time location information of transit vehicles. By predicting the movement of the transit vehicle based on continuous detection of the vehicle motion by the on-board AVL system and estimating the measures of effectiveness (MOE) of other motor vehicles based on the surveillance of traffic conditions, optimal signal timings can be obtained by solving the proposed traffic signal optimization models. Both numerical analysis and simulation tests demonstrate that the proposed system improves a transit vehicle's operation as well as minimizes its negative impacts on other motor vehicles in the traffic system. In summary, there are three major contributions of this dissertation: a) development of a novel AVL-based adaptive traffic signal control system; b) modeling of the associated traffic signal timing optimization problem, which is the key component of the proposed system; c) applications of the proposed system to two real world cases. After presenting background knowledge on two major types of transit operations, i.e., preemption and priority, traffic signal control and AVL systems, the architecture of the proposed adaptive signal control system and the associated algorithm are presented. The proposed system includes a data-base, fleet equipped with surveillance system, traffic signal controllers, a transit movement predictor, a traffic signal timing optimizer and a request server. The mixed integer quadratic programming (MIQP) and nonlinear programming (NP) are used to formulate signal timing optimization problems. Then the proposed system and algorithm are applied to two real-world case studies. The first case study concerns the SPRINTER rail transit service. The proposed adaptive signal control (ASC) system is developed to relieve the traffic congestion and to clear the accumulated vehicle queues at the isolated signal around the grade crossing, based on the location information on SPRINTER from PATH-developed cellular GPS trackers. The second case study involves the San Diego trolley system. With the information provided by the AVL system, the proposed ASC system predicts the arrival times of the instrumented trolley at signals and provides the corresponding optimal signal timings to improve the schedule adherence, thus reducing the delays at intersections and enhancing the trip reliability for the trolley travelling along a signalized corridor in the downtown area under the priority operation. The negative impact (e.g., delay increase) on other traffic is minimized simultaneously. Both numerical analysis and simulation tests in the microscopic environment are conducted using the PARAMICS software to validate the proposed system for the aforementioned applications. The results present a promising future for further field operational testing.
Compiled from papers of the 4th Biennial Workshop on DSP (Digital Signal Processing) for In-Vehicle Systems and Safety this edited collection features world-class experts from diverse fields focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. Digital Signal Processing for In-Vehicle Systems and Safety presents new approaches on how to reduce driver inattention and prevent road accidents. The material addresses DSP technologies in adaptive automobiles, in-vehicle dialogue systems, human machine interfaces, video and audio processing, and in-vehicle speech systems. The volume also features recent advances in Smart-Car technology, coverage of autonomous vehicles that drive themselves, and information on multi-sensor fusion for driver ID and robust driver monitoring. Digital Signal Processing for In-Vehicle Systems and Safety is useful for engineering researchers, students, automotive manufacturers, government foundations and engineers working in the areas of control engineering, signal processing, audio-video processing, bio-mechanics, human factors and transportation engineering.
Numerical Simulations of Physical and Engineering Process is an edited book divided into two parts. Part I devoted to Physical Processes contains 14 chapters, whereas Part II titled Engineering Processes has 13 contributions. The book handles the recent research devoted to numerical simulations of physical and engineering systems. It can be treated as a bridge linking various numerical approaches of two closely inter-related branches of science, i.e. physics and engineering. Since the numerical simulations play a key role in both theoretical and application oriented research, professional reference books are highly needed by pure research scientists, applied mathematicians, engineers as well post-graduate students. In other words, it is expected that the book will serve as an effective tool in training the mentioned groups of researchers and beyond.
The Federal Highway Administration (FHWA) Office of Operations Research and Development, located at Turner-Fairbank Highway Research Center (TFHRC), added five new research vehicles to FHWA's Innovation Research Vehicle Fleet. This fleet offers an experimental connected automation research platform that provides advanced capabilities for future operational concepts and supports their evaluation. In addition, the fleet's research platform enables full automatic control of longitudinal movements (such as acceleration and braking) with the flexibility to support lateral control (such as steering controls) for future autonomous vehicle research. Cooperative Adaptive Cruise Control (CACC) is the first operational implementation developed and tested on the new research platform. The CACC implementation will provide the ability to test the open architecture of the vehicle fleet technology platform and assess the ability of researchers to use the vehicle fleet to support the study of operational concepts and connected automation applications.
The Knowledge Solution. Stop Searching, Stand Out and Pay Off. The #1 ALL ENCOMPASSING Guide to AVL. An Important Message for ANYONE who wants to learn about AVL Quickly and Easily... ""Here's Your Chance To Skip The Struggle and Master AVL, With the Least Amount of Effort, In 2 Days Or Less..."" Automatic vehicle location (AVL or locating; telelocating in EU) is a means for automatically determining the geographic location of a vehicle and transmitting the information to a requester. Most commonly, the location is determined using GPS, and the transmission mechanism is SMS, GPRS, a satellite or terrestrial radio from the vehicle to a radio receiver. GSM and EVDO are the most common services applied, because of the low data rate needed for AVL, and the low cost and near-ubiquitous nature of these public networks. The low bandwidth requirements also allow for satellite technology to receive telemetry data at a moderately higher cost, but across a global coverage area and into very remote locations not covered well by terrestrial radio or public carriers. Other options for determining actual location, for example in environments where GPS illumination is poor, are dead reckoning, i.e. inertial navigation, or active RFID systems or cooperative RTLS systems. With advantage, combinations of these systems may be applied. In addition, terrestrial radio positioning systems utilizing an LF (Low Frequency) switched packet radio network were also used as an alternative to GPS based systems. Get the edge, learn EVERYTHING you need to know about AVL, and ace any discussion, proposal and implementation with the ultimate book - guaranteed to give you the education that you need, faster than you ever dreamed possible! The information in this book can show you how to be an expert in the field of AVL. Are you looking to learn more about AVL? You're about to discover the most spectacular gold mine of AVL materials ever created, this book is a unique collection to help you become a master of AVL. This book is your ultimate resource for AVL. Here you will find the most up-to-date information, analysis, background and everything you need to know. In easy to read chapters, with extensive references and links to get you to know all there is to know about AVL right away. A quick look inside: Automatic vehicle location, Automatic number plate recognition, Fleet telematics system, Vehicle tracking system, Intelligent transportation system, LoJack, Horizon Technologies, Mobile phone tracking, NextBus, OnStar, StarChase, Telematics, Tracking system, Vehicle infrastructure integration ...and Much, Much More! This book explains in-depth the real drivers and workings of AVL. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of AVL with the objectivity of experienced professionals - Grab your copy now, while you still can.
The vision of the Every Day Counts Adaptive Signal Control Technology (ASCT) Initiative is to mainstream the use of adaptive signal control technology. “Mainstream the use of” suggests that when traffic conditions, agency needs, resources and capability support the use of ASCT it should be implemented. These model systems engineering documents support the process of exploring the need for ASCT and articulating a set of requirements that enable agencies to specify, select, implement and test adaptive signal control technology. Over the last two decades a significant number of adaptive systems have been deactivated well before the end of their useful life due either to a lack of adequate resources or agency capability to support system operation and maintenance, or in some cases a failure to properly align agency and system operations objectives. The risks associated with ASCT implementation are significant. This document helps accomplish the tasks of clarifying objectives, identifying needs and defining constraints by leading the reader through a series of questions. The outcome of selecting and tailoring the sample responses will be a set of clear and concise statements to formulate the required systems engineering documentation. By following this guidance an agency can expect to produce the following documents: Concept of Operation, System Requirements, Verification Plan, Validation Plan.